






import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE = None     ,     )     ->    Tuple:
							_lowercase							:   List[str] 							=   {}

							if train_file is not None:
														_lowercase							:   Tuple 							=   [train_file]
							if eval_file is not None:
														_lowercase							:   Optional[Any] 							=   [eval_file]
							if test_file is not None:
														_lowercase							:   Dict 							=   [test_file]

							_lowercase							:   Dict 							=   datasets.load_dataset('csv'     ,     data_files=SCREAMING_SNAKE_CASE				)
							_lowercase							:   Dict 							=   list(ds[list(files.keys()				)[0]].features.keys()				)
							_lowercase							:   List[Any] 							=   features_name.pop(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Any 							=   list(set(ds[list(files.keys()				)[0]][label_name]				)				)
							_lowercase							:   Dict 							=   {label: i for i, label in enumerate(SCREAMING_SNAKE_CASE				)}
							_lowercase							:   Tuple 							=   tokenizer.model_input_names
							_lowercase							:   int 							=   {}

							if len(SCREAMING_SNAKE_CASE				) == 1:
														for k in files.keys():
																					_lowercase							:   List[str] 							=   ds[k].map(
																					    lambda SCREAMING_SNAKE_CASE				: tokenizer.batch_encode_plus(
																					        example[features_name[0]]     ,     truncation=SCREAMING_SNAKE_CASE     ,     max_length=SCREAMING_SNAKE_CASE     ,     padding='max_length'				)     ,     batched=SCREAMING_SNAKE_CASE     ,     )
							elif len(SCREAMING_SNAKE_CASE				) == 2:
														for k in files.keys():
																					_lowercase							:   Tuple 							=   ds[k].map(
																					    lambda SCREAMING_SNAKE_CASE				: tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])     ,     truncation=SCREAMING_SNAKE_CASE     ,     max_length=SCREAMING_SNAKE_CASE     ,     padding='max_length'     ,     )     ,     batched=SCREAMING_SNAKE_CASE     ,     )

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					_lowercase							:   str 							=   {k: v for k, v in ex.items() if k in input_names}
																					_lowercase							:   List[str] 							=   labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					_lowercase							:   str 							=   {k: v for k, v in ex.items() if k in input_names}
																					_lowercase							:   Tuple 							=   labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					_lowercase							:   int 							=   {k: v for k, v in ex.items() if k in input_names}
																					_lowercase							:   str 							=   labelaid[ex[label_name]]
																					yield (d, label)

							_lowercase							:   str 							=   (
							    tf.data.Dataset.from_generator(
							        SCREAMING_SNAKE_CASE     ,     ({k: tf.intaa for k in input_names}, tf.intaa)     ,     ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))     ,     )
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														_lowercase							:   Optional[int] 							=   train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

							_lowercase							:   int 							=   (
							    tf.data.Dataset.from_generator(
							        SCREAMING_SNAKE_CASE     ,     ({k: tf.intaa for k in input_names}, tf.intaa)     ,     ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))     ,     )
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														_lowercase							:   str 							=   val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

							_lowercase							:   List[str] 							=   (
							    tf.data.Dataset.from_generator(
							        SCREAMING_SNAKE_CASE     ,     ({k: tf.intaa for k in input_names}, tf.intaa)     ,     ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))     ,     )
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														_lowercase							:   Optional[Any] 							=   test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

							return train_ds, val_ds, test_ds, labelaid


UpperCamelCase								=		logging.getLogger(__name__)

@dataclass
class 				lowerCAmelCase_	:
				_UpperCamelCase					:       int							       = field(metadata={"help": "Which column contains the label"}			)
				_UpperCamelCase					:       str							       = field(default=__snake_case   ,		metadata={"help": "The path of the training file"}			)
				_UpperCamelCase					:       Optional[str]							       = field(default=__snake_case   ,		metadata={"help": "The path of the development file"}			)
				_UpperCamelCase					:       Optional[str]							       = field(default=__snake_case   ,		metadata={"help": "The path of the test file"}			)
				_UpperCamelCase					:       int							       = field(
				    default=128   ,		metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }   ,		)
				_UpperCamelCase					:       bool							       = field(
				    default=__snake_case   ,		metadata={"help": "Overwrite the cached training and evaluation sets"}			)




@dataclass
class 				lowerCAmelCase_	:
				_UpperCamelCase					:       str							       = field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
				_UpperCamelCase					:       Optional[str]							       = field(
				    default=__snake_case   ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
				_UpperCamelCase					:       Optional[str]							       = field(
				    default=__snake_case   ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
				_UpperCamelCase					:       bool							       = field(default=__snake_case   ,		metadata={"help": "Set this flag to use fast tokenization."}			)
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				_UpperCamelCase					:       Optional[str]							       = field(
				    default=__snake_case   ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,		)






def    __magic_name__    (	)     ->    str:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.
							_lowercase							:   Optional[int] 							=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
							_lowercase							,							_lowercase							,							_lowercase							:   Optional[int] 							=   parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir				)
							    and os.listdir(training_args.output_dir				)
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    ' --overwrite_output_dir to overcome.'				)

							# Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'     ,     datefmt='%m/%d/%Y %H:%M:%S'     ,     level=logging.INFO     ,     )
							logger.info(
							    F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1				)}, """
							    F"""16-bits training: {training_args.fpaa}"""				)
							logger.info(F"""Training/evaluation parameters {training_args}"""				)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							_lowercase							:   List[str] 							=   AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,     cache_dir=model_args.cache_dir     ,     )

							_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   int 							=   get_tfds(
							    train_file=data_args.train_file     ,     eval_file=data_args.dev_file     ,     test_file=data_args.test_file     ,     tokenizer=SCREAMING_SNAKE_CASE     ,     label_column_id=data_args.label_column_id     ,     max_seq_length=data_args.max_seq_length     ,     )

							_lowercase							:   List[Any] 							=   AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,     num_labels=len(SCREAMING_SNAKE_CASE				)     ,     labelaid=SCREAMING_SNAKE_CASE     ,     idalabel={id: label for label, id in labelaid.items()}     ,     finetuning_task='text-classification'     ,     cache_dir=model_args.cache_dir     ,     )

							with training_args.strategy.scope():
														_lowercase							:   List[str] 							=   TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path     ,     from_pt=bool('.bin' in model_args.model_name_or_path				)     ,     config=SCREAMING_SNAKE_CASE     ,     cache_dir=model_args.cache_dir     ,     )

							def compute_metrics(SCREAMING_SNAKE_CASE				) -> Dict:
														_lowercase							:   Optional[int] 							=   np.argmax(p.predictions     ,     axis=1				)

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							_lowercase							:   Optional[Any] 							=   TFTrainer(
							    model=SCREAMING_SNAKE_CASE     ,     args=SCREAMING_SNAKE_CASE     ,     train_dataset=SCREAMING_SNAKE_CASE     ,     eval_dataset=SCREAMING_SNAKE_CASE     ,     compute_metrics=SCREAMING_SNAKE_CASE     ,     )

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir				)

							# Evaluation
							_lowercase							:   Dict 							=   {}
							if training_args.do_eval:
														logger.info('*** Evaluate ***'				)
														_lowercase							:   int 							=   trainer.evaluate()
														_lowercase							:   List[Any] 							=   os.path.join(training_args.output_dir     ,     'eval_results.txt'				)

														with open(SCREAMING_SNAKE_CASE     ,     'w'				) as writer:
																					logger.info('***** Eval results *****'				)

																					for key, value in result.items():
																												logger.info(F"""  {key} = {value}"""				)
																												writer.write(F"""{key} = {value}\n"""				)

																					results.update(SCREAMING_SNAKE_CASE				)

							return results


if __name__ == "__main__":
							main()









'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   List[str]					=		logging.get_logger(__name__)

lowerCAmelCase:   Union[str, Any]					=		{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """lilt"""







    def __init__( self					:    str		,     __snake_case					:    Dict=3_05_22		,     __snake_case					:    int=7_68		,     __snake_case					:    Union[str, Any]=12		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Dict=30_72		,     __snake_case					:    Tuple="gelu"		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.1		,     __snake_case					:    List[str]=5_12		,     __snake_case					:    List[str]=2		,     __snake_case					:    str=0.02		,     __snake_case					:    Dict=1e-1_2		,     __snake_case					:    int=0		,     __snake_case					:    Any="absolute"		,     __snake_case					:    Tuple=None		,     __snake_case					:    Tuple=4		,     __snake_case					:    List[str]=10_24		,     **__snake_case					:    Union[str, Any]		,     ):
      super().__init__(pad_token_id=__snake_case		,     **__snake_case      )

      a		:   str	  =	vocab_size
      a		:   Optional[Any]	  =	hidden_size
      a		:   Union[str, Any]	  =	num_hidden_layers
      a		:   Tuple	  =	num_attention_heads
      a		:   Optional[Any]	  =	hidden_act
      a		:   Union[str, Any]	  =	intermediate_size
      a		:   Optional[Any]	  =	hidden_dropout_prob
      a		:   Tuple	  =	attention_probs_dropout_prob
      a		:   str	  =	max_position_embeddings
      a		:   Any	  =	type_vocab_size
      a		:   Union[str, Any]	  =	initializer_range
      a		:   int	  =	layer_norm_eps
      a		:   Any	  =	position_embedding_type
      a		:   Any	  =	classifier_dropout
      a		:   str	  =	channel_shrink_ratio
      a		:   Optional[Any]	  =	max_ad_position_embeddings